Combining mixture distribution and multidimensional IRTree models for the measurement of extreme response styles. (6th August 2019)

• Record Type: Journal Article
• Authors: Khorramdel, Lale; von Davier, Matthias; Pokropek, Artur
• Other Names: Khorramdel Lale guestEditor.; Jeon Minjeong guestEditor.; Leigh Wang Lihshing guestEditor.
• Is Part Of: British journal of mathematical & statistical psychology. Volume 72:Part 3(2019)
• Page Start: 538
• Page End: 559
• Publication Date: 2019-08-06
• Subjects: IRTree models -- mixture distribution models -- multidimensional item response theory -- rating scale -- response styles; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12179
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : Personality constructs, attitudes and other non‐cognitive variables are often measured using rating or Likert‐type scales, which does not come without problems. Especially in low‐stakes assessments, respondents may produce biased responses due to response styles (RS) that reduce the validity and comparability of the measurement. Detecting and correcting RS is not always straightforward because not all respondents show RS and the ones who do may not do so to the same extent or in the same direction. The present study proposes the combination of a multidimensional IRTree model with a mixture distribution item response theory model and illustrates the application of the approach using data from the Programme for the International Assessment of Adult Competencies (PIAAC). This joint approach allows for the differentiation between different latent classes of respondents who show different RS behaviours and respondents who show RS versus respondents who give (largely) unbiased responses. We illustrate the application of the approach by examining extreme RS and show how the resulting latent classes can be further examined using external variables and process data from computer‐based assessments to develop a better understanding of response behaviour and RS.